Text Summarization with Python Training Course

LangGraph is a framework designed for constructing stateful, multi-agent LLM applications as composable graphs featuring persistent state and precise execution control.

This instructor-led live training, available either online or on-site, is tailored for advanced AI platform engineers, DevOps professionals specializing in AI, and ML architects who aim to optimize, debug, monitor, and manage production-grade LangGraph systems.

Upon completion of this training, participants will be able to:

• Design and optimize intricate LangGraph topologies to enhance speed, reduce costs, and ensure scalability.
• Enhance reliability through retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
• Debug and trace graph executions, inspect states, and systematically reproduce issues encountered in production.
• Instrument graphs with logs, metrics, and traces; deploy them to production; and monitor SLAs and associated costs.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live lab environment.

Customization Options

• To arrange customized training for this course, please contact us.

Devstral is an open-source framework built for creating and operating coding agents capable of interacting with codebases, developer tools, and APIs to boost engineering productivity.

This instructor-led, live training (available online or onsite) targets intermediate to advanced ML engineers, developer-tooling teams, and SREs looking to design, implement, and optimize coding agents using Devstral.

By the end of this training, participants will be able to:

• Set up and configure Devstral for coding agent development.
• Design agentic workflows for codebase exploration and modification.
• Integrate coding agents with developer tools and APIs.
• Implement best practices for secure and efficient agent deployment.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Turkey (online or onsite) is aimed at data scientists and software engineers who wish to use Dask with the Python ecosystem to build, scale, and analyze large datasets.

By the end of this training, participants will be able to:

• Set up the environment to start building big data processing with Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance in handling large datasets.

This instructor-led, live training in Turkey (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

By the end of this training, participants will be able to:

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

Devstral and Mistral are open-source AI technologies engineered for flexible deployment, fine-tuning, and scalable integration.

This instructor-led live training (available online or onsite) targets intermediate to advanced ML engineers, platform teams, and research engineers who aim to self-host, fine-tune, and govern Mistral and Devstral models within production environments.

Upon completion of this training, participants will be able to:

• Set up and configure self-hosted environments for Mistral and Devstral models.
• Apply fine-tuning techniques to enhance domain-specific performance.
• Implement versioning, monitoring, and lifecycle governance processes.
• Ensure security, compliance, and responsible usage of open-source models.

Format of the Course

• Interactive lectures and discussions.
• Hands-on exercises focused on self-hosting and fine-tuning.
• Live-lab implementation of governance and monitoring pipelines.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

This instructor-led live training, offered online or onsite, targets developers who want to utilize the FARM stack (FastAPI, React, and MongoDB) to build dynamic, high-performance, and scalable web applications.

By the end of this training, participants will be able to:

• Configure the necessary development environment integrating FastAPI, React, and MongoDB.
• Understand the key concepts, features, and benefits of the FARM stack.
• Learn how to build REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (front end and back end) using the FARM stack.

This instructor-led, live training in Turkey (online or onsite) is targeted at developers who want to utilize FastAPI with Python to build, test, and deploy RESTful APIs with greater speed and ease.

By the end of this training, participants will be able to:

• Configure the necessary development environment for developing APIs with Python and FastAPI.
• Create APIs more quickly and easily using the FastAPI library.
• Learn how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication in APIs using the FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

LangGraph serves as a framework for constructing stateful, multi-actor LLM applications by composing graphs that maintain persistent state and allow precise control over execution flow.

This instructor-led live training, available either online or onsite, targets intermediate to advanced professionals seeking to design, implement, and manage finance solutions based on LangGraph, ensuring proper governance, observability, and regulatory compliance.

Upon completion of this training, participants will be capable of:

• Designing LangGraph workflows tailored to financial regulations and audit requirements.
• Integrating financial data standards and ontologies into graph states and tools.
• Implementing reliability, safety measures, and human-in-the-loop controls for critical processes.
• Deploying, monitoring, and optimizing LangGraph systems to meet performance, cost, and SLA targets.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live-lab environment.

Customization Options

• For requests regarding customized training for this course, please contact us to make arrangements.

LangGraph is a framework designed for constructing graph-structured Large Language Model (LLM) applications that support planning, branching, tool use, memory, and controllable execution.

This instructor-led, live training (available online or on-site) targets beginner-level developers, prompt engineers, and data practitioners who want to design and build reliable, multi-step LLM workflows using LangGraph.

By the conclusion of this training, participants will be able to:

• Explain core LangGraph concepts (nodes, edges, state) and understand when to apply them.
• Build prompt chains that branch, invoke tools, and maintain memory.
• Integrate retrieval mechanisms and external APIs into graph workflows.
• Test, debug, and evaluate LangGraph applications to ensure reliability and safety.

Format of the Course

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs within a sandbox environment.
• Scenario-based exercises focusing on design, testing, and evaluation.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

LangGraph empowers stateful, multi-agent workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, enabling interoperability, and developing decision-support systems that seamlessly align with clinical workflows.

This instructor-led live training, available online or on-site, is designed for intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.

Upon completion of this training, participants will be equipped to:

• Design healthcare-specific LangGraph workflows prioritizing compliance and auditability.
• Integrate LangGraph applications with established medical ontologies and standards, including FHIR, SNOMED CT, and ICD.
• Apply industry best practices for reliability, traceability, and explainability within sensitive environments.
• Deploy, monitor, and validate LangGraph applications in production healthcare settings.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises featuring real-world case studies.
• Implementation practice within a live laboratory environment.

Customization Options

• To request a customized version of this course, please contact us to arrange details.

LangGraph serves as a framework designed for constructing stateful, multi-agent LLM applications through composable graphs that maintain persistent state and offer precise execution control.

This instructor-led live training, available both online and onsite, targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based legal solutions equipped with essential compliance, traceability, and governance controls.

Upon completion of this training, participants will be capable of:

• Crafting legal-specific LangGraph workflows that ensure auditability and compliance.
• Integrating legal ontologies and document standards into graph state and processing mechanisms.
• Implementing guardrails, human-in-the-loop approvals, and traceable decision pathways.
• Deploying, monitoring, and maintaining LangGraph services in production environments with robust observability and cost management.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical practice.
• Hands-on implementation within a live lab environment.

Customization Options for the Course

• To request customized training for this course, please get in touch with us to arrange details.

LangGraph is a framework designed for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.

This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and product teams who wish to combine LangGraph’s graph logic with LLM agent loops to build dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.

By the end of this training, participants will be able to:

• Design graph-based workflows that coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallbacks for robust execution.
• Integrate retrieval, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and harden agent behavior for reliability and safety.

Course Format

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based design exercises and peer reviews.

Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a graph-based orchestration framework that supports conditional, multi-step workflows involving LLMs and tools, making it ideal for automating and personalizing content pipelines.

This instructor-led live training (available online or onsite) targets intermediate-level marketers, content strategists, and automation developers looking to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.

By the end of this training, participants will be able to:

• Design graph-structured content and email workflows with conditional logic.
• Integrate LLMs, APIs, and data sources for automated personalization.
• Manage state, memory, and context across multi-step campaigns.
• Evaluate, monitor, and optimize workflow performance and delivery outcomes.

Format of the Course

• Interactive lectures and group discussions.
• Hands-on labs implementing email workflows and content pipelines.
• Scenario-based exercises on personalization, segmentation, and branching logic.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Le Chat Enterprise offers a secure, customizable, and governed conversational AI platform designed for organizational use, featuring support for RBAC, SSO, connectors, and enterprise application integrations.

This instructor-led training (available online or onsite) targets intermediate-level product managers, IT leads, solution engineers, and security/compliance professionals looking to deploy, configure, and manage Le Chat Enterprise in enterprise settings.

Upon completion, participants will be able to:

• Deploy and configure Le Chat Enterprise securely.
• Implement RBAC, SSO, and compliance-driven controls.
• Connect Le Chat with enterprise applications and data repositories.
• Design and enforce governance and administrative guidelines for ChatOps.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Customization Options

• Contact us to arrange customized training for this course.

This instructor-led, live training in Turkey (online or onsite) is designed for data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

By the end of this training, participants will be able to:

• Set up the necessary environment to start developing Pandas workflows at scale with Modin.
• Understand the features, architecture, and advantages of Modin.
• Know the differences between Modin, Dask, and Ray.
• Perform Pandas operations faster with Modin.
• Implement the entire Pandas API and functions.